Steering law reveals a linear relationship between the movement time (MT) and the index of difficulty (ID) in trajectory-based steering tasks. However, it does not relate the variance or distribution of MT to ID. In this paper, we propose and evaluate models that predict the variance and distribution of MT based on ID for steering tasks. We first propose a quadratic variance model which reveals that the variance of MT is quadratically related to ID with the linear coefficient being 0. Empirical evaluation on a new and a previously collected dataset show that the quadratic variance model accounts for between 78% and 97% of variance of observed MT variances; it outperforms other model candidates such as linear and constant models; adding the linear coefficient leads to no improvement on the model fitness. The variance model enables predicting the distribution of MT given ID: we can use the variance model to predict the variance (or scale) parameter and Steering law to predict the mean (or location) parameter of a distribution. We have evaluated six types of distributions for predicting the distribution of MT. Our investigation also shows that positively skewed distribution such as Gamma, Lognormal, Exponentially Modified Gaussian (ExGaussian), and Extreme value distributions outperformed the symmetric distribution such as Gaussian and truncated Gaussian distribution in predicting the MT distribution, and Gamma distribution performed slightly better than other positively skewed distributions. Overall, our research advances the MT prediction of steering tasks from a point estimate to variance and distribution estimates, which provides a more complete understanding of steering behavior and quantifies the uncertainty of MT prediction.

Many handheld devices with stylus pens are available in the market; however, there have been few studies which examine the effects of the size of the stylus pen on user performance and subjective preferences for handheld device interfaces for various age groups. Two experiments (pen-length experiment and pen-tip width/pen-width experiment) were conducted to determine the most suitable dimensions (pen-length, pen-tip width and pen-width) for a stylus pen among young adult, child and older users. In pen-length experiment, five pen-lengths (7, 9, 11, 13 and 15 cm) were evaluated for young adult, child and older users, respectively. In pen-tip width/pen-width experiment, six combinations of three pen-tip widths (0.5, 1.0 and 1.5 mm) and two pen-widths (4 and 7 mm) were compared for young adult, child and older users, respectively. In both experiments, subjects conducted pointing, steering and writing tasks on a PDA. The results were assessed in terms of user performance and subjective evaluations for all three pointing, steering and writing tasks. We determined that the most suitable dimensions of the pen are as follows: pen-length 11–15 cm, pen-tip width 1.0–1.5 mm and pen-width 7 mm for older users; for child users, pen-length 7–13 cm, pen-tip width 1.0–1.5 mm and pen-width 4 mm; and for young adult users, pen-length 11 cm, pen-tip width 0.5 mm, and pen-width 7 mm.

The steering law is an excellent performance model for trajectory-based tasks, such as drawing and writing in GUIs. Current studies on steering tasks focus on the effect of system factors (i.e. path width and amplitude) on the movement time and steering law's related applications. We conducted a series of experiments to further explore the effect of different operational biases (bias speed or accuracy) on steering completion time and standard deviation for two steering trajectory shapes, i.e. a straight steering task and a circular steering task, and then establish a new model accommodating system and subjective factor in steering tasks. Empirical results showed that the new model is more predictive and robust than the traditional steering law.

This article presents an empirical study to determine the optimal size of a box for the input of handwriting characters on personal digital assistants (PDAs). The experiments involve the consideration of boxes for different kinds of characters, different box sizes and shapes (square and rectangular), different user postures, and the age differences of users. The results are assessed in terms of high performance (high character recognition rates, minimal stroke protrusions outside the character box, minimal number of error corrections, minimum writing time) and subjective ratings (e.g., ease of writing and minimum degree of fatigue). The results show that the optimal size of character boxes for the input of alphanumeric characters is 12 × 14 mm (rectangular), whereas for Kanji (Chinese characters) mixed with Kana characters and for Hiragana & Katakana characters the optimal size is 14 × 14 mm (square). We believe that knowledge of the optimal size of character input boxes will be useful for the design of user interfaces on PDAs.

Three experiments were conducted in this study to investigate the human ability to control pen pressure and pen tilt input, coupling cursor position, angle and scale respectively. Comparisons between pressure input and tilt input have been made in the three experiments. Experimental results show that decreasing pressure input resulted in very bad performance and was not a good input technique for any of the three experiments. In "experiment1-coupling to cursor position", the tilt input technique performed relatively better than the increasing pressure input technique, even though the tilt technique had a slightly higher error rate. In "experiment2-coupling to cursor angle", the tilt input performed relatively better than the increasing pressure input in terms of time. In "experiment3-coupling to cursor scale", tilt input performed a little better than increasing pressure input in terms of adjustment time, but a little worse in terms of the degree of control that users exerted. These results will be useful for further user interface design.